Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer vision.Brain tumor research plays a major role in medical imaging applications by providin...Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer vision.Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation.To prevent or minimize manual segmentation error,automated tumor segmentation,and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures.Many methods for detection and segmentation presently exist,but all lack high accuracy.This paper’s key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor research.Furthermore,attention concentrated on the challenges related to level set segmentation.The study proposed in this paper uses the Population-based Artificial Bee Colony Clustering(P-ABCC)methodology to reliably collect initial contour points,which helps minimize the number of iterations and segmentation errors of the level-set process.The proposed model measures cluster centroids(ABC populations)and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form,structure,and volume.The suggested model comprises of three major steps:first,pre-processing to separate the brain from the head and improves contrast stretching.Secondly,P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence contour.The level-set segmentation is then used to detect tumor regions from all volume slices with fewer iterations.Results suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS 2017.At BRATS 2019,dice progress was achieved for Entire Tumor(WT),Tumor Center(TC),and Improved Tumor(ET)by 0.03%,0.03%,and 0.01%respectively.At BRATS 2017,an increase in precision for WT was reached by 5.27%.展开更多
文摘Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer vision.Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation.To prevent or minimize manual segmentation error,automated tumor segmentation,and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures.Many methods for detection and segmentation presently exist,but all lack high accuracy.This paper’s key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor research.Furthermore,attention concentrated on the challenges related to level set segmentation.The study proposed in this paper uses the Population-based Artificial Bee Colony Clustering(P-ABCC)methodology to reliably collect initial contour points,which helps minimize the number of iterations and segmentation errors of the level-set process.The proposed model measures cluster centroids(ABC populations)and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form,structure,and volume.The suggested model comprises of three major steps:first,pre-processing to separate the brain from the head and improves contrast stretching.Secondly,P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence contour.The level-set segmentation is then used to detect tumor regions from all volume slices with fewer iterations.Results suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS 2017.At BRATS 2019,dice progress was achieved for Entire Tumor(WT),Tumor Center(TC),and Improved Tumor(ET)by 0.03%,0.03%,and 0.01%respectively.At BRATS 2017,an increase in precision for WT was reached by 5.27%.
